Technical efficiency of hospitals owned by faith based organisations in Kenya.
Kinyanjui, George Kariuki ; Gachanja, Paul Mwangi ; Muchai, Joseph Muniu 等
Introduction
The major problems facing Kenya after the colonial administration
were ignorance, diseases and abject poverty (Republic of Kenya, 2008).
The independence government embarked on promoting coverage and access to
healthcare services. Consequently by 1980, hospitals owned by Faith
Based Organisations played central role in healthcare provision
characterised by higher accessibility and affordability. Health
indicators showed rising fertility rates reaching averagely 8.1 births
for women in their fertility ages in the 1980s (Republic of Kenya,
1994). However there was a considerable drop to 5.4 by 1992 while by
2010 the total fertility rate was recorded at 4.6. This could be
attributable to pronounced population check measures alongside the
prevalence of HIV/AIDS epidemic. Infant mortality went down from 98
deaths per 1000 live births between 1974 and 1977 to around 63 deaths
per 1000 live births by 1993. By early 1990's the crude death rate
had dropped from the 20 per 1000 births recorded at independence to 12
per 1000 while the crude birth rate dropped from 50 per 1000 population
to 46 per 1000 in the same period (Owino, 1997). In 2013, crude death
rate stood at 8.19 per 1000 while the crude birth rate stood at about 40
in the same year thus giving a natural rate of increase of about 31.81
per thousand population (World Bank, 2014). Child mortality was recorded
at 93.2 deaths per 1000 live births by 1993, (Republic of Kenya, 1994;
1999). UNICEF data indicate that in 2012, the under-five mortality rate
stood averagely at 73.
(Korir, 2010) asserts the existence of inefficiency in the health
sector and that between Kshs. 1 billion and 1.4 billion in financial
terms would be salvaged if public hospitals as a group operated
efficiently. Efficiency measurements in health care are hence a vital
component in policy formulation and implementation. Despite numerous
health sector reforms and relatively sufficient financing anchored on
efforts to solve inefficiency, little has been achieved in levelling
efficiency in the Kenyan healthcare sector (Republic of Kenya, 1994).
Health care in Kenya is provided by both the public and the private
sectors. Of the 1440 private health facilities (1) recorded in 2010, 75
were hospitals owned by faith based organisations (Korir, 2010). The
Christian Health Association of Kenya, (CHAK) oversees 15 hospitals, the
Kenya Conference of Catholic Bishops, (KCCB) oversees 49 hospitals while
the Supreme Council of Kenya Muslims, (SUPKEM) runs 11 hospitals (The
Republic of Kenya, 2012) (2).
While non-governmental providers are significantly important
accounting to about 50% of all hospitals in Kenya and 36% of total
available hospital beds, 40% of these are owned by faith based
organizations (World Bank, 2010). They offer specialized healthcare with
subsidized user fees and ambient health financing mechanisms demystified
by the ability to make central decisions at unit levels with less
bureaucracy (Collins et al, 1996).
Hospitals owned by faith based organizations largely depend on
donor funding and government subsidy for their operation. However, in
the recent years, donor funding in general has significantly reduced
while regulations by the donor countries have been heightened to
facilitate efficient utilization of the donations (Karlstedt, 2010).
Government expenditure on health has remained relatively dismal
with targets such as the Abuja declaration (3) having not been met more
than a decade on owing to poor governance, high poverty levels,
inconsistency in donor funding and general treasury reluctance (World
Health Organization, 2011). Figure 1 shows the trend in government
financing as a percentage of total budget estimates to health since 1995
to 2011 and the 15% threshold set at the Abuja declaration.
[FIGURE 1.1 OMITTED]
Note the rising proportion of healthcare financing since 1995 up to
2001 (Abuja declaration) where the share of GDP directed to healthcare
declines considerably from 11% to about 6% further from the envisioned
15%.
Health sector personnel are also highly unequipped, unequally
distributed and few relative to population density. There is therefore
need for the healthcare providers to ensure efficient use of the donor
funds, government subsidies and employment of the already scarce health
personnel not only for better health care provision but also to ensure
continued support. Figures 2 and 3 represent the comparison of the
approximate number of doctors and nurses operating in Kenya County
Governments both in the public and private health facilities and the
minimum required doctors and nurses, as per WHO thresholds, in regard to
the population densities in those counties respectively.
[FIGURE 1.2 OMITTED]
[FIGURE 1.3 OMITTED]
Various governments have grappled with reversals and gains in
health system instrumentation with major policies being implemented and
amended. This paper, by employing the Data Envelopment Analysis (4)
(DEA) technique, seeks to show the technical efficiency scores of
hospitals owned by faith based organisations and to which if addressed
could assist in Kenya's healthcare provision goals.
The remainder of the paper is designed as follows: section 2 sheds
light on the theoretical and empirical underpinnings of DEA and its
application while section 3 and 4 deal with the methodology and data
source (sections 5 and 6 discuss the results and presents the concluding
remarks).
Efficiency Measurement of Healthcare Units
Firm efficiency consists of a comparison between observed and
optimal values of its outputs and inputs (Lovell, 1993). Following the
works of (Debreu, 1951) and (Koopmans, 1951), (Farrell, 1957) defines a
simple measure of firm efficiency that could account for multiple inputs
and multiple outputs. Firm efficiency consists of two components:
technical efficiency, which simply reflects the ability of a firm to
obtain maximal output from a given set of inputs, and allocative
efficiency, which basically reflects the ability of a firm to use the
inputs in optimal proportions, given their respective prices and the
production technology, (Farrell, 1957). The combination of the two
measures provides a unit measure of total economic efficiency. Whereas
there are two approaches to understanding the technical efficiency of
firms, this paper employs the input oriented approach anchored on the
assumption that the choice of which hospital to visit remains in the
spheres of a given patient. The input orientation DEA seeks to radially
contract the use of inputs while still remaining able to produce the
same output. For instance, a given hospital could be able to restructure
the composition of its labour and capital inputs while still recording
the same number of outpatients and inpatients annually.
Other methods that can be employed to estimate efficiency of
hospitals include the Stochastic Frontier Analysis (SFA). This assumes a
stochastic functional form to the frontier and thus employs econometric
techniques in obtaining the coefficients. Even though Stochastic
Frontier Analysis takes into account the stochastic noise in the data,
the initial process to specify a functional form is computationally
challenging (Gachanja et al, 2013). It is however fundamental in
conducting conventional tests of hypotheses. DEA on the other hand
dominates the non-parametric methods of estimating efficiency. The
overarching advantages of employing DEA over other methods include;
firstly, it is computationally simple and has the advantage that it can
be implemented without specifying the frontier functional form,
secondly, DEA focuses on each decision making unit in contrast to
population averages thus producing a single efficiency measure for each
decision making unit (Kirigia, 2013), thirdly, DEA can adjust for
exogenous variables that are beyond the control of the decision making
unit. Such adjustments have a strong bearing on efficiency levels of
decision making units. For instance, a health facility may be ranked
inefficient based on its inputs and outputs while say climate, civil
unrest by workers or general political instability characterized the
health sector. In themselves, the exogenous variables contract to causes
of inefficiency (Kirigia, 2013). In its variable returns to scale (VRS)
method, DEA does not require a priori knowledge of prices for the inputs
and outputs so as to compute allocative efficiency of decision making
units. Hence, tests comparing the sensitivity of Stochastic Frontier
Analysis results against those of DEA using the same data have revealed
consistency with the inefficiency scores yielded by DEA being lower than
those yielded by SFA (Korir, 2010).
Empirical Literature
Adoption and use of the Data Envelopment Analysis (DEA) technique
is gaining popularity in the third world countries and beyond. (Kirigia,
2001) investigated the technical efficiency of 155 primary health care
clinics in Kwazulu-Natal province of South Africa using Data Envelopment
Analysis. The study observed that 47 (30%) were technically efficient
while the remaining 70% were inefficient. Among the 108 technically
inefficient clinics 17 (16%) had technical efficiency score of less than
50% indicating to large extent, underemployment of the inputs. This
applied to Kwazulu-Natal clinics which had decreased input by 417 nurses
and 457 general staff. At the same time, output had increased by 115,534
antenatal visits, 1,010 births (deliveries), 179,075 child care visits,
5702 dental visits, 121,658 family planning visits, 36032 psychiatric
visits, 56068 sexually transmitted diseases visits and 34270
tuberculosis visits during the study period. This study concluded that
there was the need for more detailed studies in a number of relatively
efficient clinics to determine why they are efficient with a view to
documenting determinants of their efficiency (Kirigia, 2001).
(Kirigia et al, 2004) carried out a study on the efficiency of
public health centres in Kenya. The findings of the study showed that
44% of Kenya's Public Health Centres were technically inefficient.
Those that were technically efficient were 56% of the total.
Inefficiencies were attributable to other external factors out of the
study explanatory variables such as corruption, poor budgeting and
delayed supply of consumables.
(Masiye et al, 2006) estimated the technical, allocative and cost
efficiency among 40 health centres in Lusaka, Central and Copper-Belt
provinces of Zambia. 58% were government owned and 42%
private-for-profit enterprises. The study used the numbers of clinical
officers, nurses and other staff as inputs, and the number of outpatient
visits as output. The average technical efficiency, allocative
efficiency and cost efficiency scores for the private health centres
were 70%, 84% and 59%, respectively. These scores were 56%, 57% and 33%,
respectively, for public health centres (5). For the whole sample, the
averages were 61.9% for technical efficiency, 68.5% for allocative
efficiency and 44.5% for cost efficiency. Out of the 17 private health
centres, 5 had a technical efficiency score of 100 and 4 had allocative
efficiency and cost efficiency scores of 100%. Contrastingly, only 1 of
the 23 government health centres had all the efficiency scores of 100%.
This is an interesting outcome that may require further interest in
research.
(Amado and Santos, 2009) assessed the performance of 337 health
centres in Portugal in 2005. Assuming an input orientation of DEA, the
study considered the inputs as doctors, nurses, administrative and other
staff. The outputs were family planning consultations, maternity
consultations, consultations by patients grouped in ages of 0-18, 19-64,
and 65 and above, home doctor consultations, home nurse consultations,
curatives and other nurse treatments, injections given by a nurse, and
vaccinations given by a nurse. The mean technical efficiency score was
84.4%.
(Kirigia, 2010) using the Data Envelopment Analysis (DEA),
investigated the technical and scale efficiency of hospitals in the
republic of Benin. A sample of 23 hospitals from a zone in the Republic
of Benin with data over a period of five years, 2003-2007, was
considered.
From the study, the yearly analysis revealed that 20 (87%), 20
(87%), 14 (61%), 12 (52%) and 8 (35%) of the hospitals were inefficient
in 2003, 2004, 2005, 2006 and 2007 respectively and they needed to
either increase their output or reduce their input in order to become
technically efficient. The average variable returns to scale (VRS)
technical efficiency scores were 63%, 64%, 78%, 78% and 88% respectively
during the review period. The study also depicted that there was some
window for providing out-patient curative and preventive care and
in-patient care to extra patients without additional inputs. This would
entail leveraging of health promotion approaches and lowering of
financial barriers hindering access to health services, to boost the
consumption of underutilized health services, especially health
promotion and disease prevention.
Korir (2010) worked to measure the efficiency levels of different
categories of public hospitals in Kenya. Using DEA and Stochastic
Frontier Analysis to estimate cost efficiencies the paper found out that
productivity in Public Hospitals (PH) in Kenya increased over time while
both the Stochastic Frontier Analysis, (SFA) and Data Envelopment
Analysis, (DEA) measures of scale efficiency of 20 public hospitals
depicted that the average actual costs of the hospitals exceeded the
minimum cost by 34.31% and 27.40% respectively. If the public hospitals
as a group were operating efficiently, the savings in financial terms
would have been over KES 1 billion annually.
Sebastian and Lemma (2010) in the study of efficiency of the health
extension programmes in Tigray, Ethiopia estimated the technical
efficiency of 60 health posts. The inputs that were employed included,
the number of health extension workers and the number of voluntary
health workers. The outputs were health education sessions given by
health extension workers, pregnant women who completed three antenatal
care visits, child deliveries, number of persons who repeatedly visited
the family planning service, diarrheal cases treated in children under
five and malaria cases treated. The study revealed that fifteen (25%)
health posts were technically efficient and 38(63.3%) were operating at
their most productive scale size.
In an effort to unravel the technical efficiency of primary health
units in Kailahun and Kenema districts of Sierra Leone, (Kirigia et al,
2011) estimated the technical efficiency of samples of community health
centres (CHCs), community health posts (CHPs) and maternal and child
health posts (MCHPs). The study employed the Data Envelopment Analysis
approach on 36 MCHPs, 22 CHCs and 21 CHPs using input and output data of
2008. The findings of the study revealed that 77.8% of the MCHPs, 59.1%
of the CHCs and 66.7% of the CHPs were variable returns to scale
technically inefficient. The study further revealed significant
technical efficiencies in the use of health system resources among
peripheral health units in kailahun and Kenema districts of Sierra
Leone. As such, the study concluded that there is need to strengthen
national and district health information systems to routinely track the
quantities and prices of resources injected into the health care systems
and health service outcomes to facilitate regular efficiency analyses.
It is surprisingly of interest that much of the research around
healthcare systems efficiency has ignored facilities owned by
non-government entities. Apart from (Masiye et al, 2006) that attempted
to measure at least 42% of its sample as privately owned health
facilities, all the other studies have concentrated on public health
facilities in different parts of the world. Hence, justifications for
the inclination towards public health sector are scanty. Public health
sector has barely over 50% of coverage to the entire world's health
care demands (World Development Report, 1996). The other approximate 50%
of the demand is anticipated to be complemented by the private sector.
It is therefore a big oversight that studies endeavoring in efficiency
measurements for private facilities continually become scanty. The
vision for universal access to quality and efficient health care for
Kenyans by 2020 can only be achievable if all heath sector stakeholders
participate in the process of quality and efficient service delivery.
The world's millennium development goals to reduce infant
mortality, improve maternal health care and combat HIV/AIDS, Malaria and
other diseases require efficient allocation of health care resources by
all healthcare facilities.
Methodology
The key construct of a Data Envelopment Analysis model is the
envelopment surface (Charnes et al, 1995). The efficiency projection
path to the envelopment surface will differ depending on scale
assumption and the nature of the model; whether output or input-oriented
depending on the optimization process characterizing the firm.
For health facilities, the input-oriented model is appropriate to
determine how much input-mix the hospital would reduce and still obtain
the same output level. This is based on the assumption that the decision
to use a particular hospital or not, is the full discretion of the
patient. In such a case, output, therefore, is an exogenous variable
that the hospital management has no control over. (Banker et al, 1984)
and (Coelli et al, 2005) postulate that the DEA is a relative measure of
efficiency where the general problem is stated in the form of constant
returns to scale (CRS). This paper sets off in the spirit of (Coelli et
al, 2005) to state the (DEA) linear programming process as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the
amount of output r from hospital [MATHEMATICAL EXPRESSION NOT
REPRODUCIBLE IN ASCII] is the amount of input i to hospital j. 9r is the
weight given to output r, is weight given to input I, n is the number of
hospitals, s is number of outputs and m is number of inputs.
Also referred to as the multiplier form, this model indicates the
general presentation of the constant returns to scale DEA. Whereas the
first constraint seeks to subject that all efficiency measures be less
than or equal to one, the second constraint is imposed to make the
number of the possible solutions finite.
In employing the input orientation of DEA, this paper assumes the
dual of the generic DEA linear programming problem that seeks to
radially reduce the use of inputs while at the same time producing the
same output. Therefore, using duality, it is possible to obtain an
equivalent form of the generic DEA model as below (2).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where [phi] is a scalar whose value once obtained shows the
efficiency score for the ith hospital. It satisfies [phi] [less than or
equal to] 1, with a value of one indicating a point on the frontier
which implies that the hospital in consideration is technically
efficient (Farrel, 1957). A is a 1x1 vector of constants. Also, the
linear programming must be solved (I) times, once for each firm in the
sample. Hence, a value of [phi] will be obtained for each firm. The
intuition behind linear programming model (2) above is that the problem
seeks to radially contract the input vector, [x.sub.i] as much as
possible, while still remaining within the feasible input set. The inner
boundary of this set is a piece-wise linear isoquant which is determined
by the observed data point (i.e. all the firms in the sample size); the
radial contraction of the input vector, ([x.sub.i]) produces a projected
point (x[lambda], Q[lambda])on the surface of this technology. This
projected point is a linear combination of these observed data points.
The constraint in the problem ensures that this projected point cannot
lie outside the feasible set. According to (Fare et al, 1994), the
production technology associated with the linear programming problem
above is given as T = {(xq): q [less than or equal to] Q[lambda], x
[greater than or equal to] X[lambda]}. Furthermore, according to (Fare
et al, 1994), this technology defines a production set that is closed
and convex, and it exhibits constant returns to scale and strong
disposability.
Accounting for the variable environmental factors such as inclined
government interventions, financial constraints, labour organisation
advocacy among others we reformulate the input oriented CRS model (2) is
modified by the addition of a convexity constraint indicated as
II'[lambda] = 1. Thus, this paper assumes this empirical
foundation.
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Where II' is a 1x1 vector of ones. This approach forms a
convex hull of intersecting planes that envelope the data points more
tightly than the CRS conical hull, and therefore provides technical
efficiency scores that are greater than or equal to those obtained by
using CRS model.
Again, it should be noted that the convexity constraint is
essentially to ensure that an inefficient hospital is only benchmarked
against hospitals of similar size a feature that lack in the CRS case.
Hence, in a CRS analysis, a hospital may be benchmarked against
hospitals that are substantially larger than it, and therefore, the
X-weights sum to a value less than one.
Definition and Measurement of Variables
This paper undertakes two broad categories of variables in the
analysis; inputs and outputs. Hence, inputs include: the number of
medical officers and medical specialists (this paper defines medical
officers as doctors in charge of the health services of a civilian or
military authority or other organization while medical specialists are
defined as those doctors who have advanced qualifications in education
and clinical training on specific areas of medicine (6); the number of
nurses in individual FBO hospitals (for the purposes of this study, a
nurse was defined as those registered by the nursing council of Kenya
and provide and coordinate patient care as well as provide advice and
emotional support to patients and their family members; the number of
beds and cots in an individual facility (hospital beds are those beds
specially designed for patients admitted in the hospitals while cots are
meant for new born babies who are in need of health care; and other
hospital workers (other aggregated workers in individual health
facilities). Hence, this variable takes into consideration a pool of all
other hospital workers in all departments of individual FBO hospitals
inclusive of administrative and subordinate avoiding double enumeration
(7).
In this study in regards to outputs, it is difficult to measure the
level of patient recovery that can be attributable to the impact of an
efficient health service. Therefore inpatient and outpatient numbers in
general for any hospital in a year are used. The paper defines
inpatients as those patients recorded as admitted in the hospital
records and who occupy bed space in the hospital wards. Outpatients are
taken to be all those patients visiting the hospital for health care but
do not occupy space in the hospital wards, and hence they were not
admitted.
Data
The Kenya health Master Facilities List categorizes health
facilities into categories, type of ownership that include the Ministry
of Health, Faith Based Organizations, Non-governmental organizations and
private ownerships. The FBO hospitals are further classified as those
under the Christian Health Association of Kenya, the Kenya Conference of
Catholic Bishops and the Supreme Council of Kenya Muslims. The three
umbrella bodies oversee a total of 75 facilities made up of 15 hospitals
under the Christian Health Association of Kenya, 49 hospitals under the
Kenya Conference of Catholic Bishops and 11 under the Supreme Council of
Kenya Muslims. A simple random sample of 30 hospitals is selected for
this study translating to 40% of the population. The sample of the 30
hospitals 10, 19 and 1 hospitals from the Christian Health Association
of Kenya, the Kenya Conference of Catholic Bishops and the Supreme
Council of Kenya Muslims, respectively.
The study uses secondary data sourced from individual
hospitals' records department and from the Ministry of
Health's Master Facility List. Data on the number of beds and cots
was obtained from the Master Facility List while the rest of the input
and output variables were sourced from individual hospital records.
Collected data is tabulated in an excel sheet before analysis begin and
in a serialized format, the data is arranged in columns starting with
outputs and finally the inputs as required in the Data Envelopment
Analysis Program version II (This procedure can be done using new
commands in Stata following "st0193 from
http://www.stata-journal.com/software/sj10-2" published in the
Stata journal). As an ethical requirement, hospitals are serialized for
anonymity purposes as efficiency scores can be used to their
disadvantage.
Results and Discussions
This paper assumes the variable returns to scale in revealing that
11 (36.67%) of the hospitals were variable returns to scale technically
efficient. The 11 hospitals had technical efficiency scores of one, but
only 6 were both constant returns to scale and variable returns to scale
technically efficient. Further observation revealed that of the 11
(36.67%) technical efficient hospitals, 9 (81.82%) were categorized
under the Kenya Conference of Catholic Bishops while only 2 (18.18%)
were from the Christian Health Association of Kenya.
Hence, the Kenya Conference of Catholic Bishops facilitates the
running of all hospitals under the Catholic Church although the
individual management of these hospitals is left to the appropriating
religious congregation. These religious congregations only depend on the
Kenya Conference of Catholic Bishops for resource mobilization and
government policy adherence.
Such an arrangement gives the hospitals under the Kenya Conference
of Catholic Bishops a much higher possibility of optimal operation as
decisions are centrally made and implemented at the facility level.
Unlike the Kenya Conference of Catholic Bishops arrangement, the other
two categories have left a larger mandate on management to their
umbrella bodies. As such, centralized management presents challenges in
decision making, implementation, monitoring and evaluation. It follows
therefore that most of the Kenya Conference of Catholic Bishops
hospitals are optimally managed and could act as peers to others.
In the study, 65.33% of the hospitals were found to be technically
inefficient with the lowest scoring 0.284. This implies that the lowest
scoring hospital would reduce the use of each of its inputs by about
71.6% and still achieve the same number of inpatients and outpatients
efficiently. Therefore, additional employment of units of inputs whether
Medical officers, beds and cots, nurses or even other workers only
manifest increased costs without any changes to the output.
In the health sector, health care providers do not determine the
outputs. Management can only intervene in the use of inputs so as to
efficiently provide health care. Technical efficiency circumvents the
various avenues in which a firm can reduce the use of its inputs without
necessarily altering its output especially when the input orientation is
assumed (Kirigia, 2013). For such hospitals operating under an umbrella
body, it would raise returns if inefficient hospitals learned from their
peers on the right input mix.
The mean variable returns to scale technical efficiency was 0.79,
the intuition being that all the hospitals would averagely be expected
to reduce their use of inputs by 21%. Loosely speaking, on average the
hospitals have exceeded the resource use by 21%. Thus, if Faith Based
Organization hospitals were to operate as a group, they would have to
reduce the use of their inputs by 21 percent. Hence, actual inputs to be
reduced depend on the marginal value of each input provided by the input
slacks, thus this paper discusses the results of input slacks.
Returns to Scale
Exactly 9 (30%) of the hospitals depict increasing returns to
scale; this implies that they enjoy economies of scale with increase in
one input yielding more than unitary in output. And approximately 50% of
the Faith Based Organization hospitals experience decreasing returns to
scale and hence face diseconomies of scale where a proportionate
increase in the use of inputs increases output by less than
proportionate, and only 20 percent of the Faith Based Organization
hospitals depict constant returns to scale. Also, the majority of health
workers in Faith Based Organization hospitals such as medical officers
and nurses are on call in more than 18 hours. Hence, the majority of the
nurses are in the wards working for more than average working hours;
long hours on work duty have diminishing service returns that not only
present inefficiencies but also pose health risks to the involved
individuals.
Lovell (et al, 1990) argues that slacks may essentially be viewed
as allocative inefficiency in that they measure the resource under or
overuse. Theoretical underpinnings on input slacks especially when the
input orientation has been assumed in analysis are that inputs have to
be reduced by their marginal amounts in order for the hospital to be
efficient. In the Kenyan context, this approach would be
counterproductive since the demand for healthcare meets stiff and scarce
resource availability. Advantageously, Faith Based Organization
hospitals can use their umbrella bodies to redistribute input resources
among facilities. This way, excess inputs in efficient facilities can be
transferred to the inefficient ones. Output slacks imply to some extent
the much that the inputs have been underutilized. Table 5.2 is
insightful into this discussion.
Hospital code 27 could increase the number of outpatients by
2224.714 and inpatients by 392.624 without changing the input mix. This
could be achieved in case hospital management to intensify community
civic education on importance of seeking healthcare in both a curative
and preventive fashion. In addition, mounting of medical camps and
outreaches could utilize some of the inputs. 17 out of the 30 hospitals
do not require any output adjustments. Another key observation is that
if all the hospitals operated as a group, they would be able to increase
their outpatients and inpatients on average by 6765.181 and 30.478
respectively without changing the quantity of inputs. Inefficiency of
this nature exemplifies inadequate work-hour engagement and in staff
service input and sub-optimal utilization of other capital equipment in
the affected hospitals.
Areas for More Research
Technical efficiency is a partial measurement of total economic
efficiency. This paper proposes further research on efficiency of
healthcare facilities in Kenya. It would be informative for other
studies to employ the Stochastic Frontier Analysis method to estimate
efficiency and draw comparisons with this paper, and secondly the
extension of this paper to include other determinants of efficiency in
the availability of time and financial resources. And third, primary and
peripheral healthcare facilities owned by Faith Based Organizations have
a sizeable share of total primary healthcare provision in Kenya
considering that they cater for healthcare needs especially in remote
regions of Kenya and hence, further research would take into
consideration the estimation of their efficiency. Last, where cost data
is readily available for the health sector, further research would
revolve around cost, allocative and profit efficiencies of health
facilities in Kenya.
Conclusion
The paper analyses technical efficiency of hospitals owned by Faith
Based Organizations in Kenya. Using Data Envelopment Analysis to analyze
hospital efficiency this paper affirms the presence of inefficiency in
the healthcare sector. In conclusion, this paper has found that there
are inefficiencies in the hospitals owned by Faith Based Organizations
and that if they worked as a group their efficiency would be
approximately 79%. The inefficiency contributes into the myriad of
challenges that face the Kenyan health sector which has a bearing on the
difficulties that Kenya faces in its struggle to achieve universal
health coverage. And therefore, this paper echoes the sentiments of
(Mansfield 1999) that even with the simplicity of the secrets of
efficiency, there must be a perpetual urge to keep vigil over efficiency
of decision making units. As Boussifiane (et al, 1991) confirms, Data
Envelopment Analysis is preferable in identifying efficient operating
practices, strategies, target setting for inefficient facilities and
resource allocation, and that there is undoubtedly surmountable benefits
of estimating efficiency levels of healthcare facilities.
References
[1.] Akazili J, Adjuik M, Jehu-Appiah C, and Zere E: Using data
envelopment analysis to measure the extent of technical efficiency of
public health centres in Ghana. BMC International Health and Human
Rights 2008, 8:11 [http://www.biomedcentral.com/1472-698X/8A1].
[2.] Amado. A., Santos. S. (2009), "Challenges for performance
assessment and improvement in primary healthcare: The case of Portuguese
health centres". Health Policy Vol. 91, PP 43-56
[3.] Banker. R. D, A Charnes, W.W. Cooper (1984), "Models for
Estimating Technical and Scale Efficiencies in Data Envelopment
Analysis". Management Science vol 30, No. 9, INFORMS, pp 1078-1091
[4.] Berman P. 2001. Getting more from private health care in poor
countries: a missed opportunity. International Journal of Quality in
Health Care 13: 279-80.
[5.] Boussifiane A, Dyson RG, Thanassoulis E. Applied data
envelopment analysis. European Journal of Operational Research 1991;
52(1): 1-15.
[6.] Bruno. Y. (2006), Technical Efficiency and total Factor
Productivity Growth in Uganda's District Referral Hospitals.
Unpublished, PhD Thesis.
[7.] Channes. A, W.W Cooper, A.Y. Lewin and LM Seiford (1995), Data
Envelopment Analysis: Theory, Methodology and Application. Kluwer
Academic Publishers, Boston Dordrecht, London.
[8.] Coelli, T. J. Prasada Rao, D. S., O'Donnell, C. J. and
Battese, G. E. (2005), An Introduction to Efficiency and Productivity
Analysis, 2nd Edition, New York : Springer.
[9.] Coelli. T.J. (1996a), H Guide to DEAP Version 2.1: A Data
Envelopment Analysis Programme", Centre for Efficiency and
Productivity Analysis Working Papers University of New England Armidale.
[10.] Collins D., Quick D., Musau S., Kraushaar D. and Hussein M.
(1996). The fall and rise of cost sharing in Kenya: the impact of phased
implementation. Health Policy and Planning; 11(1): 52-63.
[11.] Debreu. G. (1951), "The Inefficiency of Resource
Utilization". Dan Econometrical Vol 19. pp. 273-292.
[12.] Fare, R., Grosskopf, S., Norris, M. and Zhang, Z. (1994),
"Productivity Growth, Technical Progress and Efficiency Changes in
Industrialized Countries", American Economic Review, 84, 66-83.
[13.] Farrel, M. J. (1957). "The Measurement of Productive
Efficiency". Journal of Royal Statistical Society, A120, 253-281.
[14.] Ferrier, G. D. and Lovell, C. A. K. (1990). "Measuring
Cost Efficiency in Banking: Econometric and Linear Programming
Evidence". Journal of Econometrics, 46, 229-245.
[15.] Gachanja, P. Wawire were, N. H. & Etyang Martin, (2013).
Total factor productivity change in the Kenyan manufacturing sector: A
Malmquist index Analysis. Herstellung, Verlag publisher, Scholars Press.
ISBN: 978-3-639-51486-5
[16.] Karlstedt, C. 2010. "Mapping Donor Conditions and
Requirements for CSO Funding: A Report Commissioned by SIDA for the
Donor Group on CSOs and Aid Effectiveness", Cecilia Karlstedt
Consulting, May 2010.
[17.] Kirigia et al.: Technical efficiency of primary health units
in Kailahun and Kenema districts of Sierra Leone. International Archives
of Medicine 2011 4:15.
[18.] Kirigia J. M. (2013) Efficiency of Health System Units in
Africa: A Data Envelopment Analysis. Nairobi: University of Nairobi
Press.
[19.] Kirigia. J. M, I. Sambo and H. Scheel (2001), "Technical
Efficiency of Public Clinics in KwaZulu-Natal Province". East
African Medical Journal 78(3). pp 511-513.
[20.] Kirigia. J.M, A. Emrouznejaed, L.G. Sambo, N. Mungufi and W.
Liambik (2004), "Using Data Envelopment Analysis to Measure the
Technical Efficiency of Public Health Centres in Kenya". Journal of
Medical Systems. vol. 28, No. 2, pp. 155-166.
[21.] Koopmans. T.C. (1951), (Ed) Activity Analysis of Production
and Allocation. New York: Wiley.
[22.] Korir, J. K. (2010). The data envelopment analysis and
stochastic frontier approaches to the measurement of hospital efficiency
in Kenya. Unpublished, Ph.D. thesis.
[23.] Lovell, C. A. K. (1993). "Production Frontiers and
Productive Efficiency". In Fried, H. O., Lovell, C. A. K., and
Schmidt, S. S. (Eds.). The Measurement of Productive Efficiency:
Techniques and Applications. Oxford: Oxford University Press.
[24.] Mansfield E. Managerial economics: theory, applications and
cases. New York: W.W. Norton & Company; 1999.
[25.] Masiye F, Kirigia JM, Emrouznejad A, Sambo LG, Mounkaila A,
Chimfwembe D, Okello D: Efficient Management of Health Centres Human
Resources in Zambia. Journal of Medical Systems 2006, 30:473-481.
[26.] Owino, W. (1997). Delivery and financing of health care
services in Kenya: critical issues and research gaps. Institute of
Policy Analysis and Research.
[27.] Republic of Kenya (1979) National Development Plan. Nairobi:
Government Printer.
[28.] Republic of Kenya (1994) National Development Plan. Nairobi:
Government Printer
[29.] Republic of Kenya Economic Survey from 1995-2012. Nairobi:
Government Printer
[30.] Sebastian, M. S., & Lemma, H. (2010). Efficiency of the
health extension programme in Tigray, Ethiopia: a data envelopment
analysis. BMC International Health and Human Rights, 10, 16.
doi:10.1186/1472-698X-10-16
[31.] Unicef Statistical Tables online:
http://www.unicef.org/infobycountry/kenya_statistics.html. Accessed 30th
January, 2014.
[32.] Valdmanis. V.G. (1990), "Ownership and Technical
Efficiency of Hospitals". Journal of Medical Care 28(6) pp. 552-561
[33.] World Bank (1994), Better Health in Africa: Experiences and
Lessons Learned Development in Practice. World Bank, pp 23-31.
[34.] World Bank. 1996. World Development Report 1996: From Plan to
Market. New York: Oxford University Press. World Bank.
https://openknowledge.worldbank.org/handle/10986/5979 License: CC BY 3.0
IGO.
[35.] World Bank. 2010. World Development Report 2010: Environment
and Climate Change. World Bank.
[36.] World Bank. 2014. World Development Report 2014: Jobs. New
York: World Bank.
[37.] World Health Organisation: World Health Statistics report.
Geneva 2010.
by
George Kariuki Kinyanjui.
School of Economics, University of Cape Town, South Africa
Cape Town, South Africa
&
Paul Mwangi Gachanja, Ph.D.
Senior Lecturer of Economics, School of Economics, Kenyatta
University, Kenya
Nairobi, Kenya
&
Joseph Muniu Muchai, Ph.D.
Lecturer of Economics, School of Economics, Kenyatta University,
Kenya
Nairobi, Kenya
Notes
(1) This includes all levels of private health facilities from
dispensaries, health centres, nursing homes to national hospitals.
(2) Christian Health Association of Kenya, Kenya Conference of
Catholic Bishops and Supreme Council of Kenya Muslims are the major
Faith Based Organizations blocks with centralised healthcare management
systems for all institutions affiliated to them
(3) In 2001, the African heads of state arrived at a declaration
that member states would increase government financing to healthcare to
at least 15% of the total government budget.
(4) This is a linear programming model that measures efficiency
levels of firms (Decision Making Units) that have multi-input and
multi-output variables. It is non-parametric.
(5) Those owned by the government and supervised by the Ministry of
Health.
(6) Definition borrowed from World Health Organization. Examples of
medical specialists include addiction psychiatrist, adolescent medicine
specialist, allergist (immunologist) etc.
(7) The staff enumeration avoided recounting workers in varied
categories such as where a medical officer served simultaneously as the
facility manager, or a nurse who coupled up as the facility secretary,
the phenomena was not very observable though.
Table 5.1: Results on CRS TE, VRS TE scores and hospital
returns to scale
Hospital Code CRS TE VRS TE RETURNS TO SCALE
1 0.355 0.45 Increasing Returns to Scale
2 0.474 0.475 Decreasing Returns to Scale
3 0.653 0.863 Increasing Returns to Scale
4 0.364 0.706 Decreasing Returns to Scale
5 0.6 1 Increasing Returns to Scale
6 0.696 0.785 Decreasing Returns to Scale
7 0.399 1 Decreasing Returns to Scale
8 0.321 0.689 Decreasing Returns to Scale
9 1 1 --
10 0.517 1 Decreasing Returns to Scale
11 0.294 0.772 Decreasing Returns to Scale
12 0.391 0.55 Increasing Returns to Scale
13 1 1 --
14 1 1 --
15 1 1 --
16 0.215 0.338 Decreasing Returns to Scale
17 1 1 --
18 0.545 0.794 Decreasing Returns to Scale
19 0.439 0.712 Decreasing Returns to Scale
20 0.28 0.284 Increasing Returns to Scale
21 0.715 0.743 Increasing Returns to Scale
22 0.634 0.651 Decreasing Returns to Scale
23 1 1 --
24 0.396 0.973 Decreasing Returns to Scale
25 0.594 0.632 Decreasing Returns to Scale
26 0.297 0.849 Decreasing Returns to Scale
27 0.242 0.35 Increasing Returns to Scale
28 0.954 1 Increasing Returns to Scale
29 0.602 1 Increasing Returns to Scale
30 0.671 0.743 Decreasing Returns to Scale
Mean 0.588 0.779
Table 5.2: Amounts of inputs available for reallocation
and output increase potentials
Hospital Beds and Medical Nurses
Code cots officers
1 0 0 2.4
2 3.4 0.9 11.0
3 21.7 0.2 12.6
4 0 3.3 0
5 0 0 0
6 0 1.5 1.3
7 0 0 0
8 54.7 0.4 0
9 0 0 0
10 0 0 0
11 0 0.9 0
12 12.2 0.6 0
13 0 0 0
14 0 0 0
15 0 0 0
16 0 0.7 5.0
17 0 0 0
18 0 2.8 5.1
19 0 0 0
20 0 0 3.3
21 0 4.2 8.0
22 59.9 0.0 14.0
23 0 0 0
24 104 4 0
25 28.5 3.3 17.7
26 47.7 9.1 0
27 0 0.5 0
28 0 0 0
29 0 0 0
30 0.0 0.5 7.6
Mean
Hospital Other Outpatients Inpatients
Code Workers
1 9.1 2237 398
2 0 0 0
3 0 5012.4 0
4 18.3 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 5.4 24939.5 0
9 0 0 0
10 0 0 0
11 0 11823.3 0
12 0.4 4782.7 0
13 0 0 0
14 0 0 0
15 0 0 0
16 0 0 0
17 0 0 0
18 56.9 31608.3 0
19 0 20761 0
20 29.2 0 0
21 23.9 0 15.2
22 0 0 0
23 0 0 0
24 99.2 26415.3 0
25 0 0 8.5
26 60.3 56345.7 0
27 0 2224.7 392.6
28 0 0 0
29 0 0 0
30 0 0 0
Mean 6765.2 30.5
